1. Field of the Invention
The subject of this invention is the remote identification of postures assumed by a person by utilizing the signals continuously provided by a sensor which he/she wears. It involves the classification of the states successively assumed by the sensor and the person. A notable difficulty is to utilize abundant but not very descriptive signals since the postures are complex, subject to variations and to transient states and that they do not lend themselves to direct measurements.
2. Description of the Related Art
In the art of classification, for each event to be classified a certain number of characteristics are defined, called parameters which are used for the classification and which may stem from sensors or from other pieces of information. The events should be distributed into categories called classes according to the values assumed by these parameters. Certain classification methods are accomplished automatically, by a computer having logic or digital comparison and decision tools.
Two main methods exist. In the first, classification resorts to explicit rules which cause the membership of a class to depend on the value of a particular parameter or a group of parameters. For example, an event is assigned to a class if a parameter reaches a value above a threshold, or if the sum of one group of parameters is above a threshold, the value of which is defined by one skilled in the art. With such methods it is possible to identify certain postures as this will be seen, but their effectiveness is not sufficient and many portions of the signals from the sensor cannot be identified.
The other group of known methods uses what is called a learning base comprising a certain number of events. The computer must then define the classification rules, which remain implicit to the operator according to the values of the parameters. In a first so-called non-supervised learning alternative, the computer itself distributes the events of the base into classes according to similarities or distances between the events, and the definition of the classes remains unknown. In another so-called supervised learning alternative, the operator indicates the class of each event of the learning base, and the task of the computer is to define a digital or logic function which observes the classification decided by the operator according to the parameters of the events. It is also possible to combine both alternatives in order to obtain therefrom a third so-called semi-supervised learning alternative wherein membership of a class is defined by the operator only for some of the events of the learning base (in practice, in small number as compared with the others).
The common drawback to all these methods is the difficulty of establishing good rules, capable of minimizing the proportion of poorly classified events. With explicit rules defined by the operator, many events remain indeterminate in practice because it is difficult to establish specific and detailed rules by observation or intuition; in the learning methods, the automatically defined rules will be inaccurate if the examples added into the learning base are poorly representative of future events, and sometimes the operator's lack of control over these implicit rules will not allow him/her to correct them. Further, building up a learning base by hand requires time and may involve errors. Finally, such a table will not be upgradeable.